Hypoglycemia risk with physical activity in type 1 diabetes: a data-driven approach

Autor: Sahana Prasanna, Souptik Barua, Alejandro F. Siller, Jeremiah J. Johnson, Ashutosh Sabharwal, Daniel J. DeSalvo
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Frontiers in Digital Health, Vol 5 (2023)
Druh dokumentu: article
ISSN: 2673-253X
DOI: 10.3389/fdgth.2023.1142021
Popis: Physical activity (PA) provides numerous health benefits for individuals with type 1 diabetes (T1D). However, the threat of exercise-induced hypoglycemia may impede the desire for regular PA. Therefore, we aimed to study the association between three common types of PA (walking, running, and cycling) and hypoglycemia risk in 50 individuals with T1D. Real-world data, including PA duration and intensity, continuous glucose monitor (CGM) values, and insulin doses, were available from the Tidepool Big Data Donation Project. Participants' mean (SD) age was 38.0 (13.1) years with a mean (SD) diabetes duration of 21.4 (12.9) years and an average of 26.2 weeks of CGM data available. We developed a linear regression model for each of the three PA types to predict the average glucose deviation from 70 mg/dl for the 2 h after the start of PA. This is essentially a measure of hypoglycemia risk, for which we used the following predictors: PA duration (mins) and intensity (calories burned), 2-hour pre-exercise area under the glucose curve (adjusted AUC), the glucose value at the beginning of PA, and total bolus insulin (units) within 2 h before PA. Our models indicated that glucose value at the start of exercise and pre-exercise glucose adjusted AUC (p
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